ultralytics 8.0.141 create new SettingsManager (#3790)

docs/datasets/segment/index.md

@@ -71,10 +71,10 @@ The `train` and `val` fields specify the paths to the directories containing the
 !!! example ""
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
 
@@ -82,7 +82,7 @@ The `train` and `val` fields specify the paths to the directories containing the
         model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
         ```
     === "CLI"
-    
+
         ```bash
         # Start training from a pretrained *.pt model
         yolo detect train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
@@ -137,4 +137,4 @@ auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt
 
 The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
 
-By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
+By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.